How Does Machine Learning Work?

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Machine learning allows computers to perform tasks without being programmed to do them. It’s a branch of artificial intelligence that uses algorithms to identify patterns in data, make decisions, and even predict future outcomes. 

Machine learning determines what products you’re most likely to buy and what TV series you’re most likely to watch and enjoy. In other words, it’s a technology that helps businesses get insight from raw data. According to a 2020 survey by Deloitte, more than 67% of companies have already adopted ML technologies, and 97% plan to integrate them into their existing software solutions in the upcoming year. 

While not everyone needs to know all the technical details about machine learning, it’s better to understand what the technology does and what it can or cannot do. Let’s find out what machine learning is and how it works before considering how an ML-powered software solution would fit your business. 

Machine Learning Overview 

Machine learning uses the capabilities of machines to imitate intelligent human behavior without direct programming. In fact, it’s an attempt to teach computers to think, learn, and act like humans. Machine learning helps computers learn, grow, and improve from experiences by themselves. Other technologies are used for getting the most out of collected data. 

The concept of machine learning can be traced back to World War II. The Bombe or the Turing machine can be considered the earliest example of a machine learning algorithm. In fact, machine learning was first defined by Arthur Samuel in 1959.

By granting computers the ability to learn and grow, businesses have experienced several major benefits:

  • Improved business operations 
  • More insights into customer behavior 
  • Adaptability to changing market conditions 
  • A greater understanding of customer and business needs 

How Does Machine Learning Work?

Machine learning finds, analyzes, and learns from data patterns to make better predictions using new data sets. ML resembles the way we learn and improve. While making a decision, we consider our past experiences and assess the situation. A machine learning algorithm does the same. It analyzes past data to make decisions or predictions. In other words, machine learning is an AI-enabled mechanism that allows machines to self-learn and improve over time. 

Machine learning starts with data. It requires massive amounts of data to be trained. The more data the system analyzes, the better the outcomes. Once the data sets are ready, a machine learning model can train itself and make predictions or find patterns. During the training period, programmers can help the model and push it in the right direction to achieve more accurate results. 

Three categories of machine learning

  • Supervised machine learning. The following ML models are trained with labeled data sets that allow for more accurate outcomes over time. For example, an algorithm can be trained with pictures of cats and other animals labeled by humans, and the algorithm is able to find its own way to identify pictures of cats. 
  • Unsupervised machine learning. In this case, a model analyzes the data sets and looks for patterns on its own. Unsupervised machine learning can find hidden patterns or trends that people aren’t even looking for. For example, an unsupervised machine learning model can look through sales data and identify specific types of clients.  
  • Reinforcement machine learning. Machines are trained through trial and error like we train our dogs. Programmers help the model make the best decision by establishing a reward system.


Where To Apply Machine Learning

Machine learning is a technology used in various fields such as cyber fraud detection, online recommendation engines from Netflix, Google and Amazon, Google’s self-driving cars, etc. The following branch of artificial intelligence has immense potential when it comes to its predictive abilities, task automation, and large-scale data analysis.  

According to the latest report by BusinessWorldIT, the machine learning market will grow to $30.6 billion by 2024. If the trend continues gaining momentum, we’ll see a massive use of machine learning algorithms in almost every industry. 

Use cases of machine learning in 2022:

  • User behavior analysis 

The retail sector uses machine learning algorithms to analyze user behavior. They can help build more customer-oriented sales strategies and predict emerging trends. Also, ML can help companies stay one step ahead of the market, improve customer experiences, and reduce churn rates. It can also identify hidden patterns and behaviors that aren’t visible to humans and offer businesses a more in-depth picture of their business processes and how to change them to drive more business. 

With ML-powered chatbots, online stores, as well as any other businesses, can provide customer service 24/7.  

  • Task automation 

ML has been used to automate a number of mundane and repetitive tasks across various business sectors. It’s widely used in manufacturing, where it greatly reduces manufacturing defects and increases efficiency and scalability. In the agricultural sector, ML can help farmers predict and decipher different data sets. ML is also used for automating tedious manual data entry. Automated data entry allows employees to focus more on high-value tasks. 

  • Image recognition

This is one of the tasks where machines can outperform humans. ML-powered algorithms are second-to-none when it comes to recognizing and classifying images. Thanks to ML-based computer vision, machines can perceive visual content nearly as well as humans.  

  • Security 

With a massive ecosystem of digital information, businesses need to follow various prevention and control mechanisms to ensure the security of user and business data. They implement firewalls, intrusion prevention systems, threat management solutions, and other systems to ensure top-of-the-line security. When a company system is dealing with sensitive information, dedicated security teams monitor, update and fix any vulnerabilities long before would-be intruders can exploit them.

ML can take the load of monitoring and vulnerability assessment tasks off the backs of security teams and enhance overall data security. For example, ML can analyze hundreds of emails and reduce spam or eliminate any suspicious emails. 

To Sum Up 

Machine learning is rapidly gaining popularity and has become a critical technology for solving complex business problems and improving efficiency. There are numerous business sectors where ML has been successfully implemented. 

At Unicsoft, we know how to harness the power of ML algorithms and transform them into solid business benefits. Drop us a line and get a free estimate for your project.